A blackboard architecture for control
Artificial Intelligence
The blackboard model of problem solving
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Goal relationships resulting from the initial data and subsequent processing can he used to dynamically construct a partial topology of the solution space based on what appear to be feasible solutions. This structure can be used to make control decisions that significantly reduce the amount of search required to solve a problem in a complex domain. We examine the utility of this approach in the context of a multi-level, cooperative knowledge source model of problem solving. We present a taxonomy of goal relationships for constructing partial topologies of the solution space and show that mechanisms using this information can be built as natural extensions of an integrated data-directed and goal directed archi tecture. Examples and performance results demonstrating how these additions improve the system's ability to evaluate potential activities are provided.